NUTRIclock · a tool based on microbiome and artificial intelligence for implementing precision nutrition.

NUTRIclock: a tool based on microbiome and artificial intelligence for implementing precision nutrition.

Aging is the decline of physiological properties that affect all organisms. It is an unavoidable process and constitutes the greatest risk factor for the development of chronic diseases like neurodegenerative disorders or cancer. According to the WHO, in 2020 the number of people over 60 years old overcame those under 5 years, and the number of people over 80 years old will triple between 2020-2050. Also, this demographic shift is not a high-income country problem exclusively as low- and middle-income countries are also experiencing these changes. Thus, this increase in life expectancy makes it extremely urgent to understand and develop mechanisms to promote healthy aging. Nutrition has traditionally been a non-pharmacological mean to influence human physiology and has demonstrated effectiveness at slowing aging progression via different processes like modulating metabolism, lowering inflammation and/or influencing on the gut microbiome. The gut microbiome that comprises all gut microorganisms, is strongly influenced by nutrition and has shown capable to interact and modulate these aging-related mechanisms as well. The NUTRIclock project proposes the generation of a “nutritional clock”, an aging clock influenced by microbiome and nutrition profiles to evaluate how their changes may modulate aging process. This clock will calculate the biological age of a subject based on this data in contrast to the chronological age. To achieve that, we proposed combining advanced Artificial Intelligence (AI) algorithms known as Neural Networks (NNs) with metagenomic and nutritional data. The NNs provide great efficiency handling large and complex datasets as well as extracting patterns from non-linear data correlations. Thus, NUTRIclock project will provide a tool to evaluate the effect of nutritional interventions on the biological age of a patient and therefore their effectiveness, helping to promote healthy aging. Also, due to NNs capacity to manage large datasets, this will allow to process higher amounts of information per patient. Facilitating the further development of tailored nutritional recommendations for a specific individual profile and advancing in the personalized nutrition field.

Data: CORDIS, © European Union